class_docstr: An implementation of `"FGSD" <https://papers.nips.cc/paper/6614-hunt-for-the-unique-stable-sparse-and-fast-feature-learning-on-graphs>`_
  from the NeurIPS '17 paper "Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs".
  The procedure calculates the Moore-Penrose spectrum of the normalized Laplacian.
  Using this spectrum the histogram of the spectral features is used as a whole graph representation.
  
  Args:
      hist_bins (int): Number of histogram bins. Default is 200.
      hist_range (int): Histogram range considered. Default is 20.
      seed (int): Random seed value. Default is 42.
docstr: Fitting a FGSD model.
  
  Arg types:
      * **graphs** *(List of NetworkX graphs)* - The graphs to be embedded.
docstr_tok: ["Fitting","a","FGSD","model",".","Arg","types",":","*","*","*","graphs","*","*","*","(","List","of","NetworkX","graphs",")","*","-","The","graphs","to","be","embedded","."]
code: def fit(self, graphs: List[nx.classes.graph.Graph]):
      """
      Fitting a FGSD model.
  
      Arg types:
          * **graphs** *(List of NetworkX graphs)* - The graphs to be embedded.
      """
      self._set_seed()
      graphs = self._check_graphs(graphs)
      self._embedding = [
          self._calculate_fgsd(graph) for graph in graphs
      ]
  
file_path: karateclub/karateclub/graph_embedding/fgsd.py
imports: import numpy
  import networkx
  from typing import List
  from karateclub.estimator import Estimator
label_desc: Use image node_id 3 for calling the FGSD obj's underlying member method code with example usage: obj.fit(graphs) without return types